Bibliographic record
Abstract
The successful launch and diffusion of new drugs is an essential factor for the survival of many pharmaceutical firms. To ensure the success of a new drug, sophisticated managers in this industry require decision-support tools. This review presents an overview of such strategic and analytical tools, based on significant contributions by marketing scientists. The review is organized according to the components of a launch and diffusion decision chain which represents the sequence of decisions that must be made when launching a new drug. This includes methods for gauging the commercial potential of a new treatment over time, pricing and promotion strategies to maximize value, and leveraging potential across different countries. This review provides an overview of current methods and possible directions for future advances in the field. The successful launch and diffusion of new therapies are key factors in the success of pharmaceutical firms. To ensure the success of a new drug, sophisticated managers in this industry require decision-support tools. This review provides an overview of such strategic and analytical tools, based on significant contributions by marketing scientists. This includes methods for gauging the commercial potential of a new treatment over time, pricing and promotion strategies to maximize value, and leveraging potential across different countries. This review is organized according to the components of a launch and diffusion decision chain, which covers the essential decisions to be made when launching a new drug.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".